H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 118 Citations 63,202 326 World Ranking 55 National Ranking 36

Research.com Recognitions

Awards & Achievements

2018 - IEEE Fellow For contributions to apprenticeship and reinforcement learning for robotics and autonomous systems

2011 - Fellow of Alfred P. Sloan Foundation

2011 - Hellman Fellow

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Reinforcement learning, Robot, Artificial neural network and Machine learning. The Artificial intelligence study combines topics in areas such as Markov decision process and Computer vision. The concepts of his Reinforcement learning study are interwoven with issues in Principle of maximum entropy, Mathematical optimization, Benchmark, Function and Key.

When carried out as part of a general Robot research project, his work on Motion planning is frequently linked to work in Task analysis, therefore connecting diverse disciplines of study. His work carried out in the field of Artificial neural network brings together such families of science as Feature engineering, Control, Training set and Set. His study in the fields of Supervised learning under the domain of Machine learning overlaps with other disciplines such as Meta learning.

His most cited work include:

  • Model-agnostic meta-learning for fast adaptation of deep networks (1969 citations)
  • Apprenticeship learning via inverse reinforcement learning (1878 citations)
  • Trust Region Policy Optimization (1849 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Reinforcement learning, Robot, Machine learning and Artificial neural network are his primary areas of study. His research ties Computer vision and Artificial intelligence together. His Reinforcement learning study incorporates themes from Human–computer interaction, Control, Mathematical optimization, Set and Sample.

He combines subjects such as Object, Control engineering, Trajectory and Domain with his study of Robot. His Machine learning research incorporates themes from Generalization and Adaptation. A large part of his Artificial neural network studies is devoted to Supervised learning.

He most often published in these fields:

  • Artificial intelligence (54.69%)
  • Reinforcement learning (41.15%)
  • Robot (29.86%)

What were the highlights of his more recent work (between 2019-2021)?

  • Reinforcement learning (41.15%)
  • Artificial intelligence (54.69%)
  • Machine learning (21.70%)

In recent papers he was focusing on the following fields of study:

Pieter Abbeel spends much of his time researching Reinforcement learning, Artificial intelligence, Machine learning, Human–computer interaction and Code. The study incorporates disciplines such as SIGNAL, Control, Set, Robot and Sample in addition to Reinforcement learning. His Robot study combines topics from a wide range of disciplines, such as Object, Field and Navigation system.

His Artificial intelligence research incorporates elements of Generalization and State. His studies deal with areas such as Encoder and Representation as well as Machine learning. His Human–computer interaction study combines topics in areas such as Teleoperation and Plan.

Between 2019 and 2021, his most popular works were:

  • CURL: Contrastive Unsupervised Representations for Reinforcement Learning (113 citations)
  • Reinforcement Learning with Augmented Data (59 citations)
  • Denoising Diffusion Probabilistic Models (45 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Pieter Abbeel mainly focuses on Artificial intelligence, Reinforcement learning, Machine learning, Code and Generalization. The various areas that Pieter Abbeel examines in his Artificial intelligence study include Stability and Pattern recognition. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Domain, Control, Feature learning, Adaptation and Sample.

Pieter Abbeel has researched Domain in several fields, including SIGNAL, Human–computer interaction, Object, Robot and Key. His specific area of interest is Machine learning, where Pieter Abbeel studies Artificial neural network. His biological study deals with issues like Generative grammar, which deal with fields such as Contextual image classification.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Trust Region Policy Optimization

John Schulman;Sergey Levine;Pieter Abbeel;Michael Jordan.
international conference on machine learning (2015)

2675 Citations

Apprenticeship learning via inverse reinforcement learning

Pieter Abbeel;Andrew Y. Ng.
international conference on machine learning (2004)

2331 Citations

Trust Region Policy Optimization

John Schulman;Sergey Levine;Philipp Moritz;Michael I. Jordan.
arXiv: Learning (2015)

2095 Citations

End-to-end training of deep visuomotor policies

Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)

1742 Citations

InfoGAN: interpretable representation learning by information maximizing generative adversarial nets

Xi Chen;Yan Duan;Rein Houthooft;John Schulman.
neural information processing systems (2016)

1286 Citations

Model-agnostic meta-learning for fast adaptation of deep networks

Chelsea Finn;Pieter Abbeel;Sergey Levine.
international conference on machine learning (2017)

1250 Citations

Discriminative probabilistic models for relational data

Ben Taskar;Pieter Abbeel;Daphne Koller.
uncertainty in artificial intelligence (2002)

802 Citations

An Application of Reinforcement Learning to Aerobatic Helicopter Flight

Pieter Abbeel;Adam Coates;Morgan Quigley;Andrew Y. Ng.
neural information processing systems (2006)

655 Citations

A Survey of Research on Cloud Robotics and Automation

Ben Kehoe;Sachin Patil;Pieter Abbeel;Ken Goldberg.
IEEE Transactions on Automation Science and Engineering (2015)

628 Citations

High-Dimensional Continuous Control Using Generalized Advantage Estimation

John Schulman;Philipp Moritz;Sergey Levine;Michael Jordan.
arXiv: Learning (2015)

623 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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